Multi-Threaded Programming 3: Locking, Lock-Free, Wait-Free

Original Author: Joseph Simons

Now I want to cover the basic terminology used when talking about concurrent algorithms. This will be helpful so that you are familiar with the available techniques you have when you have multiple threads operating together. The term concurrent itself refers to an algorithm that can scale from being synchronous on a single thread to utilizing multiple threads at the same time. The terms I am going to cover also exist independently of any architecture, though the actual implementation will vary due to the underlying hardware differences. I am going to cover them in order of increasing difficulty to implement, so if you are interested in implementing an algorithm of your own, I would suggest starting with the simplest implementation and moving on only as needed.

If you want to read up on other things that I have covered, then here are my previous posts in this series:

Locking

In the previous post, we ended with an example of a mutex lock. This is the easiest and most straightforward method available in order to restrict access to a particular area of code for only a single thread at a time. However, as you may have surmised, it will also have the worst performance when dealing with many threads all accessing the same area at once. This is because the threads are restricted to operating serially, so you basically have given up on the performance benefits of using multiple threads for the area where you lock. But if it is an area that is accessed infrequently, or there is only a small chance that multiple threads will be in the locked area at the same time, it can easily be a “good enough” solution.

Locking is a good first step when you are dealing with data that can be read and modified by multiple threads. As we saw with the explanation of caches, when you modify data on one thread that change will quickly propagate to other threads that then read that data. However, the trickiness lies in the fact that these modifications can occur at any point within your algorithm. So by forcing the algorithm to operate in a serial manner we prevent any unexpected changes that can occur and disrupt what we expect to happen.

While debugging mutex locks can be fairly simple (as far as threading problems go), there are several ways that things can go wrong. If you are using locks and find that program has stopped responding, simply pause it and examine it in the debugger. Chances are that one of the following three things has occurred.

The first pitfall you might have fallen into is what is referred to as a deadlock. This occurs when the operation of your program results in such a manner where a lock is obtained but is never released. This causes your program to halt because no further progress can be made because all the threads are waiting on something that is never going to happen.

One cause of this can be due to not acquiring locks in the same manner in different places in your code. So if you acquire Lock A then Lock B in one place and Lock B then Lock A in another, you can get in a situation where one has Lock A locked and tried to lock Lock B, but can’t because Lock B has already been acquired. Since it has Lock A already, the thread that is holding Lock B cannot get it and so never unlocks Lock B. If you acquire the locks always in the Lock A then Lock B direction, then you can prevent this problem from ever occurring.

Another cause is simply attempting to use the same lock in multiple places, but you have code that calls to lock multiple times without a resulting unlock. You can easily design a re-entrant mutex that can handle this sort of use case if desired, but make sure that this behavior is what you want.

The final case that I will cover is likely the hardest to ascertain. When you have multiple threads of different priorities, you need to be careful when it comes to sharing locks. If you have a case where there is low priority, medium priority, and high priority thread, and the low and high priority threads both execute in the same critical section, then you need to be wary of random boosting in Windows, that can help avoid this situation for you.

Lock-Free

When using atomic operations, we are also forcing a linear execution on our program, but in a much smaller scope. However, they do not fall prey to the things that can go wrong when using locks. Therefore, if we are not using any locks, then our program will always be able to advance. If our algorithm is guaranteed to never wait (even if it has to repeat some of its work), then it is considered a lock-free algorithm. Another way of thinking about it is if you were to arbitrarily pause any thread that is in your algorithm, it will not affect any of the other threads in there.

However, when dealing with lock-free algorithms, even though you do not need to worry about deadlocks as described above, you have a whole new set of problems. The main issue when writing code that doesn’t force linear execution for large sections is that almost anything can happen at anytime. When thinking through the code, you have to take into account that any other thread can come along and perform operations, such as changing a variable, adding / removing a node, or reorganizing the data. Because of this, it is common with lock-free algorithms to continuously test the assumptions before performing an action. If the assumed state is incorrect, then lock-free algorithms typically will loop and repeat their work until they can safely proceed. So, if an area of code has low contention, then using locks might actually lead to faster execution.

However, just testing the state isn’t without its caveats. One particularly nasty thing that can happen is known as the ABA problem. The idea here is that even if you are making sure that you are operating on the same address, by testing it with a Compare-And-Swap atomic action, it can still fail. If the address was recycled in some way (such as being deleted than allocated again, or popped then pushed again) then even though it is at the same location, it is not the same data. This can lead to crashes or lost data, and is very difficult to track down due to the fact that it can be extremely rare.

Wait-Free

Now, if you have an algorithm that will never repeat work and will always advance, then you have a wait-free algorithm. Because of their properties, they will always finish in a fixed number of steps. These can be exceedingly difficult to implement and it is rare to actually see them in use, though technically any algorithm can be certain circumstances). This means that for games, where performance is typically the highest goal and the number of threads is low, lock-free algorithms are generally the best option for highly threaded, time-critical areas.

Final Considerations

Never underestimate the power of a “good enough” solution. Because multi-threaded programming carries with it a high chance of hard to detect bugs, a tiny bit of performance tradeoff for a more stable codebase can definitely be worth it. Therefore, it is actually good practice to start with using simple locks to protect shared data. Then, when you are certain that those locks are a bottleneck, investigate into lock-free solutions. Also, because writing good lock-free algorithms can be difficult, it is usually a good idea to keep an eye out for already implemented libraries that of. These will help reduce possible implementation errors, since simple tests might fail to catch all the problems that can occur. Especially since the issues can be very dependent on rare, timing specific situations.

It is also a very good idea to redesign your code so that you minimize the areas where multiple threads can interact with each other. This gives you the benefits of parallelism without many the issues that can occur (such as all those described earlier). We will look at ways to do this later in the series, but I wanted the reader to first be familiar with some of the difficulties that can come with multi-threaded programming. That way, if you encounter a situation where you have to worry about multiple threads executing on the same data, you have a good grasp of the problem space going into it.

Next Time

For the next post, we are going to look at a basic locking queue implementation. For the post after that, we will then see a lock-free queue implementation where I will cover why we need to make the changes that we did in order to convert it. For the following post, we will see if we can optimize the lock-free queue further, and some pitfalls to watch out for while doing so. Because of the difficulties with wait-free algorithms and the fact they typically don’t have much real-world benefit, we will not be covering those further.

Further Reading

If you want to read some more about locking, lock-free, and wait-free algorithms, feel free to check out the following areas: